Big data analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, and customer preferences. Big data refers to data that is too large to be handled by traditional data processing systems. Sources of big data include social media, sensors, mobile devices, and call detail records. Big data is used in various domains like business intelligence, design and engineering. Reasons for the importance of big data include increasing innovation, improving customer satisfaction, sharpening competitive advantages, enabling narrow customer segmentation, and reducing downtime.

1. Submitted By-
D.ASHWIN

2. What is Big data analytics ?

3. Big data analytics is the process
of examining large data sets to
uncover hidden patterns,
unknown correlations, market
trends, customer preferences
and other useful business
information.

4. Big Data Definition
•Big data means that the data is unable to be handled and
processed by most current information system or
methods
•Most of the traditional data mining methods or data
analytics developed for a centralized data analysis
process may not be able to be applied directly to big
data.

5. Sources of Big Data

8. Application domains of Big Data

10. Big Data in Business Intelligence

11. The Evolution of Business Intelligence
scale
scale
1990’s 2000’s 2010’s

12. OLTP: OnlineTransaction Processing (DBMSs)
OLAP: Online Analytical Processing (Data
Warehousing)
RTAP: Real-Time Analytics Processing (Big Data
Architecture & technology)

13. Big data in design and engineering

14. •Engineering department of manufacturing
companies.
•Boeing’s new 787 aircraft is perhaps the best
example of Big Data, a plane designed and
manufactured.
•Big Data needs to be transferred for
conversion into machining related information
to allow the product to be manufactured.

15. Reasons for the importance of Big
Data

16. •Increase innovation and development of next generation
product
•Improve customer satisfaction
•Sharpen competitive advantages
•Create more narrow segmentation of customers
•Reduce downtime